The Experts below are selected from a list of 89376 Experts worldwide ranked by ideXlab platform
Yiannis Aloimonos - One of the best experts on this subject based on the ideXlab platform.
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Computer Vision and Natural Language Processing
ACM Computing Surveys, 2016Co-Authors: Peratham Wiriyathammabhum, Douglas Summers-stay, Cornelia Fermüller, Yiannis AloimonosAbstract:Integrating computer vision and Natural Language Processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and Natural Language Processing in multimedia and robotics applications with more than 200 key references. The tasks that we survey include visual attributes, image captioning, video captioning, visual question answering, visual retrieval, human-robot interaction, robotic actions, and robot navigation. We also emphasize strategies to integrate computer vision and Natural Language Processing models as a unified theme of distributional semantics. We make an analog of distributional semantics in computer vision and Natural Language Processing as image embedding and word embedding, respectively. We also present a unified view for the field and propose possible future directions.
Dragomir R. Radev - One of the best experts on this subject based on the ideXlab platform.
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Graph-based Natural Language Processing and Information Retrieval
2011Co-Authors: Rada Mihalcea, Dragomir R. RadevAbstract:Graph theory and the fields of Natural Language Processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of Natural Language Processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information Processing tasks. Readers will come away with a firm understanding of the major methods and applications in Natural Language Processing and information retrieval that rely on graph-based representations and algorithms.
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Graph-based Natural Language Processing and information retrieval
Electrical Engineering, 2011Co-Authors: Rada Mihalcea, Dragomir R. RadevAbstract:"This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval"- "Graph theory and the fields of Natural Language Processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of Natural Language Processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information Processing tasks. Readers will come away with a firm understanding of the major methods and applications in Natural Language Processing and information retrieval that rely on graph-based representations and algorithms"-
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Natural Language Processing
Encyclopedia of Library and Information Science, 2003Co-Authors: Elizabeth D. Liddy, Jiaxiang Lin, Lucy Vanderwende, John Prager, Dragomir R. Radev, Eduard Hovy, Ralph WeischedelAbstract:Natural Language Processing. Encyclopedia of Library and Information Science, pages 2126–2136, 2003.
Peratham Wiriyathammabhum - One of the best experts on this subject based on the ideXlab platform.
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Computer Vision and Natural Language Processing
ACM Computing Surveys, 2016Co-Authors: Peratham Wiriyathammabhum, Douglas Summers-stay, Cornelia Fermüller, Yiannis AloimonosAbstract:Integrating computer vision and Natural Language Processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and Natural Language Processing in multimedia and robotics applications with more than 200 key references. The tasks that we survey include visual attributes, image captioning, video captioning, visual question answering, visual retrieval, human-robot interaction, robotic actions, and robot navigation. We also emphasize strategies to integrate computer vision and Natural Language Processing models as a unified theme of distributional semantics. We make an analog of distributional semantics in computer vision and Natural Language Processing as image embedding and word embedding, respectively. We also present a unified view for the field and propose possible future directions.
Rada Mihalcea - One of the best experts on this subject based on the ideXlab platform.
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Multilingual Natural Language Processing
2012Co-Authors: Rada MihalceaAbstract:With rapidly growing online resources, such as Wikipedia, Twitter, or Facebook, there is an increasing number of Languages that have a Web presence, and correspondingly there is a growing need for effective solutions for multilingual Natural Language Processing. In this talk, I will explore the hypothesis that a multilingual representation can enrich the feature space for Natural Language Processing tasks, and lead to significant improvements over traditional solutions that rely exclusively on a monolingual representation. Specifically, I will describe experiments performed on three different tasks: word sense disambiguation, subjectivity analysis, and text semantic similarity, and show how the use of a multilingual representation can leverage additional information from the Languages in the multilingual space, and thus improve over the use of only one Language at a time. This is joint work with Samer Hassan and Carmen Banea.
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Graph-based Natural Language Processing and Information Retrieval
2011Co-Authors: Rada Mihalcea, Dragomir R. RadevAbstract:Graph theory and the fields of Natural Language Processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of Natural Language Processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information Processing tasks. Readers will come away with a firm understanding of the major methods and applications in Natural Language Processing and information retrieval that rely on graph-based representations and algorithms.
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Graph-based Natural Language Processing and information retrieval
Electrical Engineering, 2011Co-Authors: Rada Mihalcea, Dragomir R. RadevAbstract:"This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval"- "Graph theory and the fields of Natural Language Processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of Natural Language Processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for Natural Language Processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information Processing tasks. Readers will come away with a firm understanding of the major methods and applications in Natural Language Processing and information retrieval that rely on graph-based representations and algorithms"-
Douglas Summers-stay - One of the best experts on this subject based on the ideXlab platform.
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Computer Vision and Natural Language Processing
ACM Computing Surveys, 2016Co-Authors: Peratham Wiriyathammabhum, Douglas Summers-stay, Cornelia Fermüller, Yiannis AloimonosAbstract:Integrating computer vision and Natural Language Processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and Natural Language Processing in multimedia and robotics applications with more than 200 key references. The tasks that we survey include visual attributes, image captioning, video captioning, visual question answering, visual retrieval, human-robot interaction, robotic actions, and robot navigation. We also emphasize strategies to integrate computer vision and Natural Language Processing models as a unified theme of distributional semantics. We make an analog of distributional semantics in computer vision and Natural Language Processing as image embedding and word embedding, respectively. We also present a unified view for the field and propose possible future directions.
